Software Engineer - Human Motion Data

Apptronik•Austin, TX

11h•Onsite

About The Position

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of embodied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better. As a Software Engineer- Human Motion Data, you will leverage your background in robotics to build the crucial link between human-data and our reinforcement learning pipelines. This role is dedicated to architecting robust motion data pipelines—integrating diverse sources like mocap, game engines (like Unreal or Unity), teleoperation, and generative AI motion models to generate thousands of rich, physically accurate human motion trajectories. You will apply your deep expertise in kinematics and rigid body dynamics to translate raw human movement into actionable, dynamically feasible data for whole-body reinforcement learning. As a core member of the Motion Control and Planning team, you will work closely with Controls stakeholders to play a key role in maintaining a high-velocity, ego-free engineering culture while ensuring our humanoid robots move with unprecedented fluidity.

Requirements

Strong theoretical and practical understanding of robot kinematics (FK/IK), coordinate transformations, and rigid body dynamics.
Experience building or maintaining pipelines for spatial data, including motion capture, teleoperation tracking, or AI-driven motion generation.
Expertise in Python for scripting, data processing, and pipeline automation.
A results-oriented mindset with an eagerness to bridge the gap between human motion data and real-world robotic control algorithms.
High adaptability and a willingness to explore new tools, moving seamlessly across different layers of the robotics software stack as project needs evolve.
A BS or MS degree in Robotics, Mechanical Engineering, Computer Science, or a related highly technical field.
2+ years of industry or applied research experience in robotics, motion planning, sim-to-real pipelines, or technical animation data generation.
A proven track record of processing large amounts of spatial or motion data to drive robotic or simulated systems.

Nice To Haves

Hands-on experience with state-of-the-art motion generation models and open-source retargeting libraries (e.g., GMR, Omni-retarget).
Proficiency in C++ is highly valued to help integrate with our broader robotics software stack.
Familiarity and hands-on experience utilizing 3D game engines (Unreal Engine or Unity) or advanced physics simulators for robotics data generation or simulation.
Hardware (HW) experience , particularly working directly with physical robotic platforms.
A strong portfolio showcasing relevant robotics, sim-to-real, or motion generation projects.

Responsibilities

Design, build, and maintain end-to-end motion data pipelines, integrating diverse sources such as motion capture (mocap), teleoperation , and synthetic generation using diffusion models, animation and gaming engines , to support humanoid robot development.
Implement and optimize kinematic and dynamic retargeting pipelines to accurately map human demonstrations onto the robot's specific physical constraints, mass distributions, and joint limits.
Develop tools and scripts to process and clean raw human demonstration data, and apply state-of-the-art retargeting libraries (e.g., GMR, Omni-retarget) to synthesize and filter new behaviors.
Leverage game engines (Unreal Engine or Unity) and physics simulators to build simulated environments for procedural motion generation and data augmentation.
Generate high-volume, high-quality trajectory datasets required for training whole-body reinforcement learning policies.
Write robust, automated pipelines to streamline data flow between human demonstration sources, generative motion models, game engines, and the RL training infrastructure.
Collaborate closely with the Reinforcement Learning and Controls teams to iterate on data requirements, understand failure modes, and ensure the generated trajectories are physically viable on hardware.